Introduction

Introduce yourself. What’s your background? Could you code before Data School? What did your daily work pattern look like before Data School? Etc. This section will be placed in an introductory block at the top of the page separate from the rest of the content. Don’t change the section title from “Introduction” otherwise this process won’t work correctly.

My Project

Rather than yourself, this is the space to introduce your project. What are your goals, what was your data, how do you plan to work with it? Perhaps show some example data if it would help.

In order to build this demo poster correctly, you will also need to have installed the tidyverse, gapminder, and kableExtra packages.

Preliminary results

This section will demonstrate the different visuals you might want use to show off your project. Don’t feel the need to go overboard, this is supposed to give a taste of the work you are doing rather than being a publication ready document.

To get tables formatting correctly, use knitr::kable to convert the table to html format. If you also want to have alternate row highlighting, pass the result to kable_styling('striped') from the kableExtra package.

Tables

knitr::kable(head(gapminder, n = 5), format = "html", caption = "A table of data") %>% 
  kable_styling("striped")

Table 1: A table of data

country	continent	year	lifeExp	pop	gdpPercap
Afghanistan	Asia	1952	28.801	8425333	779.4453
Afghanistan	Asia	1957	30.332	9240934	820.8530
Afghanistan	Asia	1962	31.997	10267083	853.1007
Afghanistan	Asia	1967	34.020	11537966	836.1971
Afghanistan	Asia	1972	36.088	13079460	739.9811

Images from a file

Plots from R

Figure 1: Yet another gapminder plot

Your figure and table captions are automatically numbered and can be referenced in the text if needed: see eg. Table 1 and Figure 1

My Digital Toolbox

What digital tools have you been using in your project? Which ones have you learned since starting Data School?

You can use all the usual R markdown features in writing a project summary, including lists:

• R - dplyr, ggplot, …
• Python
• SQL

My time went …

What parts of the project took the most time and effort? Were there any surprising challenges you encountered, and how did you solve them?

Next steps

What further steps do you wish your project could take? Or are there any new digital skills that you are keen to develop as a result of your involvement in the Data School?

My Data School Experience

This poster is mostly about your synthesis project. However we would also like to hear about other parts of your Data School experience. What aspects of the program did you really enjoy? How have you been applying the skills you have learned in your daily work? Have you been able to transfer this knowledge to your team members? Concrete examples demonstrating this would be useful here (meetings/talks/collaborations/new roles). Any descriptions of the personal impact the program has had are welcome here as well!